CN109828311B - Limestone structure-cause type identification method and equipment based on logging information - Google Patents
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- 235000019738 Limestone Nutrition 0.000 title claims abstract description 88
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Abstract
The embodiment of the invention provides a limestone structure-cause type identification method and device based on logging information. The method comprises the following steps: analyzing the natural gamma log GR to obtain a parameter with the highest correlation degree with the GR and a parameter with the lowest correlation degree with the GR, determining a standard well drilling, and standardizing the parameter with the highest correlation degree and the lowest correlation degree with the GR in the non-standard well drilling by adopting the parameter with the highest correlation degree and the lowest correlation degree with the GR in the standard well drilling; acquiring a corresponding scatter diagram according to the parameter with the highest GR correlation degree in the standard well drilling, the parameter with the lowest GR correlation degree in the standard well drilling, the parameter with the highest GR correlation degree in the standardized non-standard well drilling and the parameter with the lowest GR correlation degree in the standardized non-standard well drilling; and identifying the limestone structure-cause type by combining the range threshold of the parameter with the highest and the lowest correlation degree with GR according to the corresponding scatter diagram. The method and the equipment provided by the embodiment of the invention can effectively identify the limestone structure-cause type.
Description
Technical Field
The embodiment of the invention relates to the technical field of geological science, in particular to a limestone structure-cause type identification method and device based on logging information.
Background
Carbonate rock refers to sedimentary rock composed primarily of carbonate minerals, primarily including limestone and dolomite, and also frequently with terrestrial detritus and clay into various transitional types of rock. Limestone is an important type of carbonate rock, often with complex lithology. The structure-cause classification of common limestone is an important method for dividing lithology according to the structure and components of the limestone, and representative classification schemes include Fock classification and Dunhamer classification. Particularly, Dunhamel divides the limestone without biological bonding effect in the original components during deposition into 4 types of granular limestone, mud-granular limestone, granular marl, marl and the like, and is widely accepted and used in the industry because the marl is simple and easy to use and simultaneously reflects the hydrodynamic force of the deposition visually and clearly. Domestic oil and gas exploration is continuously deepened into the field of marine carbonate rocks, limestone in the stratums of three marine basins in China has the characteristics of old age, deep burial, strong diagenesis transformation and the like, and various complicated lithological types are difficult to distinguish.
The method for identifying limestone based on logging curves is mainly a qualitative intersection chart method, and comprises the steps of making an intersection chart by using optimal logging parameters and extracting main components by using a main component analysis method to make an intersection chart. The method is characterized in that the optimized logging parameter making intersection map is generally effective in identifying different types of carbonate rocks, such as dolomite, cloud-containing limestone, limestone and cloud limestone in the carbonate rocks by utilizing the intersection of DEN/DT value and PE value (DEN: compensation density; DT: acoustic time difference; PE: photoelectric absorption cross section index), but the overlapped area is more in view of the distribution of different numerical points on the scatter diagram; meanwhile, a method of U/(Th + K) and Th + K (U: uranium; Th: thorium; K: potassium) intersection is used, so that the particle limestone can be only marginally separated, and the resolution is limited. The method for analyzing the main components considers a plurality of logging parameters simultaneously, and prepares a cross plot by a method for extracting the main components, which is considered to be beneficial to improving the resolution of the lithology of the carbonate rock, but from the practical effect, the method only has better resolution on the large categories of the carbonate rock such as siliceous cloud rock, limestone rock and algae cloud rock, and has poor resolution on the structure-cause type of the limestone rock; meanwhile, the classification scheme simultaneously uses various logging parameters, so that the classification scheme is easily interfered by various factors, and the extraction of the main components is abnormal, so that the method is difficult to be applied to distinguishing the limestone structure-cause type. Therefore, finding a method based on well logging data, which can resist interference of various factors and keep effective identification of limestone structure-cause type, is a technical problem that is widely concerned in the industry.
Disclosure of Invention
In view of the above problems in the prior art, embodiments of the present invention provide a method and an apparatus for identifying a limestone structure-cause type based on well log data.
In a first aspect, an embodiment of the present invention provides a limestone structure-cause type identification method based on well log data, including: analyzing the natural gamma log GR to obtain a parameter with the highest GR relevance and a parameter with the lowest GR relevance, determining a standard well, and standardizing the parameter with the highest GR relevance in the non-standard well and the parameter with the lowest GR relevance in the non-standard well by adopting the parameter with the highest GR relevance in the standard well and the parameter with the lowest relevance in the standard well; acquiring a corresponding scatter diagram according to the parameter with the highest GR correlation degree in the standard well drilling, the parameter with the lowest GR correlation degree in the standard well drilling, the parameter with the highest GR correlation degree in the standardized non-standard well drilling and the parameter with the lowest GR correlation degree in the standardized non-standard well drilling; and identifying the limestone structure-cause type according to the corresponding scatter diagram by combining the range threshold of the parameter with the highest GR correlation degree and the range threshold of the parameter with the lowest GR correlation degree.
Further, the parameters with the highest correlation with GR include: uranium, potassium or thorium.
Further, the parameters with the lowest correlation to GR include: uranium, potassium or thorium.
Further, the normalizing the parameter with the highest correlation to GR in the standard well and the parameter with the lowest correlation to GR in the standard well and the parameter with the highest correlation to GR in the non-standard well and the parameter with the lowest correlation to GR in the non-standard well comprises: obtaining the average value of the parameter with the highest GR relevance degree in the standard well drilling, obtaining the average value of the parameter with the highest standard well drilling and the average value of the parameter with the lowest GR relevance degree in the standard well drilling, obtaining the average value of the parameter with the lowest standard well drilling; obtaining an average value of a parameter with the highest GR correlation degree in a non-standard well, obtaining an average value of the highest parameter of the non-standard well, and an average value of a parameter with the lowest GR correlation degree in the non-standard well, obtaining a minimum parameter average value of the non-standard well, obtaining a normalization coefficient of the parameter with the highest GR correlation degree in the non-standard well according to the average value of the highest parameter of the standard well and the average value of the highest parameter of the non-standard well, and obtaining a normalization coefficient of the parameter with the lowest GR correlation degree in the non-standard well according to the average value of the lowest parameter of the standard well and the average value of the lowest parameter of the non-standard; normalizing the parameter with the highest GR correlation in the non-standard well according to the normalization coefficient of the parameter with the highest GR correlation in the non-standard well, and normalizing the parameter with the lowest GR correlation in the non-standard well according to the normalization coefficient of the parameter with the lowest GR correlation in the non-standard well.
Further, the range threshold of the parameter with the highest GR correlation includes: less than 1.2 uranium, greater than 1.2 uranium and less than 2.5 uranium, or greater than 2.5 uranium.
Further, the range threshold of the parameter with the lowest GR correlation includes: a uranium to thorium ratio of less than 1.5, a uranium to thorium ratio of greater than 1.5 to less than 4, or a uranium to thorium ratio of greater than 4.
In a second aspect, an embodiment of the present invention provides a limestone structure-cause type identification device based on well log data, including:
the parameter standardization module is used for analyzing the natural gamma logging GR, obtaining a parameter with the highest GR relevance and a parameter with the lowest GR relevance, determining a standard well, and standardizing the parameter with the highest GR relevance in the non-standard well and the parameter with the lowest GR relevance in the non-standard well by adopting the parameter with the highest GR relevance in the standard well and the parameter with the lowest GR relevance in the standard well;
the scatter diagram acquisition module is used for acquiring a corresponding scatter diagram according to the parameter with the highest GR correlation degree in the standard well drilling, the parameter with the lowest GR correlation degree in the standard well drilling, the parameter with the highest GR correlation degree in the standardized non-standard well drilling and the parameter with the lowest GR correlation degree in the standardized non-standard well drilling;
and the limestone structure-cause type identification module is used for identifying the limestone structure-cause type according to the corresponding scatter diagram by combining the range threshold of the parameter with the highest GR correlation and the range threshold of the parameter with the lowest GR correlation.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor to invoke the program instructions to perform the method for log-based limestone structure-cause type identification provided by any of the various possible implementations of the first aspect.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method for log-based limestone structure-cause type identification provided in any one of the various possible implementations of the first aspect.
According to the limestone structure-cause type identification method and device based on logging information, provided by the embodiment of the invention, the limestone structure-cause type can be effectively identified by the logging information by acquiring the parameter with the highest correlation degree with GR and the parameter with the lowest correlation degree with GR in a standard well drilling, standardizing the parameter with the highest correlation degree with GR and the parameter with the lowest correlation degree with GR in a non-standard well drilling on the basis of the parameter, then constructing a scatter diagram according to the standardized parameters and combining the range threshold of the corresponding parameters.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief description will be given below to the drawings required for the description of the embodiments or the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flowchart of a method for identifying limestone structure-cause type based on well log data according to an embodiment of the present invention;
FIG. 2 is a graph illustrating GR and U average values for various limestone types provided by an embodiment of the present invention;
FIG. 3 is a cross scatter plot based on U and U/Th provided by the embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating lithology of Ordovician limestone sections in a region of the Tarim basin according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a limestone structure-cause type identification device based on well log data according to an embodiment of the present invention;
fig. 6 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. In addition, technical features of various embodiments or individual embodiments provided by the invention can be arbitrarily combined with each other to form a feasible technical solution, but must be realized by a person skilled in the art, and when the technical solution combination is contradictory or cannot be realized, the technical solution combination is not considered to exist and is not within the protection scope of the present invention.
The embodiment of the invention provides a limestone structure-cause type identification method based on logging information, and referring to fig. 1, the method comprises the following steps:
101. analyzing the natural gamma log GR to obtain a parameter with the highest GR relevance and a parameter with the lowest GR relevance, determining a standard well, and standardizing the parameter with the highest GR relevance in the non-standard well and the parameter with the lowest GR relevance in the non-standard well by adopting the parameter with the highest GR relevance in the standard well and the parameter with the lowest relevance in the standard well; generally, the content of GR (natural gamma logging), Th (thorium), K (potassium) and U (uranium) satisfies: granular limestone < marl. Referring specifically to fig. 2, it can be seen from fig. 2 that the GR average value 201 includes: 28.7 for granular limestone, 33.1 for argillite, 36.5 for granular/marquisite; the U average 202 includes: 1.7 for granular limestone, 2.1 for argillite, 2.9 for granular/marquisite. The U values are generally derived primarily from adsorption of U in sedimentary bodies of water, and the Th and K values are generally derived from sedimentary clays. Generally, the sedimentary hydrodynamics of the granular limestone is the strongest and the sedimentation rate is the largest, and then the sedimentary hydrodynamics of the marlite is the weakest and the sedimentation rate is the slowest. The clay content is reduced in turn, but in some environments the clay content is very low and cannot indicate the strength of the hydrodynamic force based on the content of Th and K alone. Meanwhile, under the condition of consistent water body environment, the deposition rate determines the enrichment degree of U, and the slower the deposition is, the more U is enriched, and vice versa. Therefore, when the lithology is difficult to distinguish between the Th and K values, it is also very effective to reflect the different lithologies according to the U value, usually the U value of the granular limestone is the lowest, followed by the marl and the marl, and the U value of the marl is the largest. In summary, it is necessary to find out the difference of the contribution degree of U, Th and K to GR value by finding out various correlation degrees.
102. Acquiring a corresponding scatter diagram according to the parameter with the highest GR correlation degree in the standard well drilling, the parameter with the lowest GR correlation degree in the standard well drilling, the parameter with the highest GR correlation degree in the standardized non-standard well drilling and the parameter with the lowest GR correlation degree in the standardized non-standard well drilling;
103. and identifying the limestone structure-cause type according to the corresponding scatter diagram by combining the range threshold of the parameter with the highest GR correlation degree and the range threshold of the parameter with the lowest GR correlation degree.
Before step 101, the method further includes a step of obtaining samples of limestone with different structural cause types, specifically: through the identification of core slices of different wells and different depths, samples (N >100) of limestone with different structural cause types are obtained, and the cores are reset according to lithology and lithology sequence characteristics.
On the basis of the foregoing embodiment, in the limestone structure-cause type identification method based on logging information provided in the embodiment of the present invention, the parameter with the highest correlation with GR includes: uranium, potassium or thorium.
On the basis of the foregoing embodiment, in the limestone structure-cause type identification method based on logging information provided in the embodiment of the present invention, the parameter with the lowest correlation degree with GR includes: uranium, potassium or thorium.
In general, the parameter with the highest GR correlation may be uranium, and the parameter with the lowest GR correlation may be thorium. However, in practice the situation may be reversed, or there may be other elements, for example, potassium, which may be the most or the least relevant parameter to GR. Specific details can be found in table 1, which shows the correlation between each element and natural gamma log in table 1.
TABLE 1
As can be seen from the observation of Table 1, GR has the best correlation with U, and the correlation value reaches 0.9151; GR has little correlation with Th, and the correlation value is only-0.0533.
On the basis of the foregoing embodiments, the method for identifying a limestone structure-cause type based on logging information provided in the embodiments of the present invention includes normalizing, by using a parameter with the highest GR correlation in a standard well and a parameter with the lowest GR correlation in the standard well, a parameter with the highest GR correlation in a non-standard well and a parameter with the lowest GR correlation in the non-standard well, the method including: obtaining the average value of the parameter with the highest GR relevance degree in the standard well drilling, obtaining the average value of the parameter with the highest standard well drilling and the average value of the parameter with the lowest GR relevance degree in the standard well drilling, obtaining the average value of the parameter with the lowest standard well drilling; obtaining an average value of a parameter with the highest GR correlation degree in a non-standard well, obtaining an average value of the highest parameter of the non-standard well, and an average value of a parameter with the lowest GR correlation degree in the non-standard well, obtaining a minimum parameter average value of the non-standard well, obtaining a normalization coefficient of the parameter with the highest GR correlation degree in the non-standard well according to the average value of the highest parameter of the standard well and the average value of the highest parameter of the non-standard well, and obtaining a normalization coefficient of the parameter with the lowest GR correlation degree in the non-standard well according to the average value of the lowest parameter of the standard well and the average value of the lowest parameter of the non-standard; normalizing the parameter with the highest GR correlation in the non-standard well according to the normalization coefficient of the parameter with the highest GR correlation in the non-standard well, and normalizing the parameter with the lowest GR correlation in the non-standard well according to the normalization coefficient of the parameter with the lowest GR correlation in the non-standard well. Specifically, the step of normalizing the parameter most closely associated with GR in the non-standard well and the parameter least closely associated with GR in the non-standard well can be detailed as follows:
1. selecting the well with the most slice identification samples as a standard well, and firstly calculating the average value of the well A (namely the parameter with the highest GR correlation degree in the standard well) of the marlite samples of the standard well as calculating the average value as WASThe average value of well B (i.e., the parameter with the lowest GR correlation in standard wells) of the marl samples is calculated as WBS；
2. Calculating a normalization coefficient: average value W of log C (i.e., parameter most highly correlated to GR in non-standard well) of marlite samples from non-standard well xCxAnd the average value W of log D (i.e., the parameter that correlates least well to GR in a non-standard well)DxThe normalized coefficient of the log C of the non-standard well x is RCx＝WCx/WASThe normalized coefficient of the log D of the non-standard well x is RDx＝WDx/WBS；
3. The log C, D for each sample of the non-standard well x was normalized: the well log C of each sample after standardization is CxS＝Cx/RCxNormalized D log value DxS＝Dx/RDx；
4. According to the methods 2 to 3, the normalization coefficients of the non-standard wells in which all the samples are located are obtained, and the log values of all the samples are normalized.
On the basis of the steps 1 to 4, the well logging C after well drilling standardization can be obtainedxSAnd DxSRatio C ofxS/DxSIn combination with CxSPreparation and obtaining of CxS(usually U) and CxS/DxS(usually U/Th) intersection scatter plots.
On the basis of the foregoing embodiment, in the limestone structure-cause type identification method based on logging information provided in the embodiment of the present invention, the threshold value of the range of the parameter with the highest GR correlation degree includes: less than 1.2 uranium, greater than 1.2 uranium and less than 2.5 uranium, or greater than 2.5 uranium.
On the basis of the foregoing embodiment, in the limestone structure-cause type identification method based on logging information provided in the embodiment of the present invention, the threshold value of the range of the parameter with the lowest GR correlation degree includes: a uranium to thorium ratio of less than 1.5, a uranium to thorium ratio of greater than 1.5 to less than 4, or a uranium to thorium ratio of greater than 4.
The range threshold values of the various parameters can be specifically shown in table 2, and the relationship between the lithology of limestone and the ratio of uranium to thorium is given in table 2.
TABLE 2
The range thresholds for the various parameters in table 2, in combination with the scatter plot described previously, can be seen in fig. 3. Fig. 3 includes: GS (representing granular limestone, squares in fig. 3), PS (representing argillite, diamonds in fig. 3), and MS (representing granular/marmite, triangles in fig. 3). As can be seen in FIG. 3, U <1.2 and U/Th <1.5 are within the range of granular limestone, 1.2< U <2.5 and 1.5< U/Th <4 are within the range of granular limestone, U >2.5 and U/Th >4 are within the range of granular/marbled limestone. The number of sample data points totaled 138.
According to the limestone structure-cause type identification method based on the logging information provided by the embodiments of the invention, lithology interpretation is carried out on the Ordovician limestone section in a certain area of the Tarim basin, and the subsequent period is calibrated by rock debris slices of continuous sampling (5m intervals) of the whole well section, so that the accuracy of the obtained lithology interpretation scheme is more than 85 percent. On the basis, fine sedimentary microfacies research is carried out, and important basis is provided for guiding oil and gas exploration in the region. Specifically, referring to fig. 4, fig. 4 includes: U/Th (0-15), GR (0-50), U (0-5), well depth (unit: m, range 5840 meters to 6020 meters), well interpretation profile, cuttings slice revision profile, modification, original lithology profile 1 and original lithology profile 2. As can be seen from comparison between the well logging interpretation profile and the rock fragment slice revision profile, the limestone structure-cause type identification method based on the well logging information provided by the embodiment of the present invention can achieve a better effect on the identification accuracy, and only 4 places need to be modified (shown as 1, 2, 3, and 4 places in the modification column in fig. 4). Wherein the bottom of fig. 4 lists the types of limestone symbolized by the symbols, in order from left to right: granular limestone, marl limestone, granular marl limestone, algal limestone and granular debris marl limestone.
According to the limestone structure-cause type identification method based on the logging information, provided by the embodiment of the invention, the limestone structure-cause type can be effectively identified by virtue of the logging information by acquiring the parameter with the highest correlation degree with GR and the parameter with the lowest correlation degree in the standard well drilling, standardizing the parameter with the highest correlation degree with GR and the parameter with the lowest correlation degree in the non-standard well drilling on the basis of the parameter, then constructing a scatter diagram according to the standardized parameters and combining the range threshold values of the corresponding parameters.
It should be noted that the parameter with the highest or lowest correlation with GR described in the embodiments of the present invention refers to whether the limestone GR is mainly generated by a certain parameter. For example, the GR of limestone is predominantly produced by the element U (uranium) (i.e., highly correlated) and substantially not (or rarely) by the element Th (thorium) (i.e., less correlated). Of course, this example is only for the purpose of illustration of the correlation level, and it is not to be understood that the correlation level is determined by the elements Th and U (and possibly by other elements having radioactivity).
The implementation basis of the various embodiments of the present invention is realized by programmed processing performed by a device having a processor function. Therefore, in engineering practice, the technical solutions and functions thereof of the embodiments of the present invention can be packaged into various modules. Based on this reality, on the basis of the embodiments, the embodiments of the present invention provide a limestone structure-cause type identification device based on logging information, which is used for executing the limestone structure-cause type identification method based on logging information in the above method embodiments. Referring to fig. 5, the apparatus includes:
the parameter standardization module 501 is configured to analyze the natural gamma log GR to obtain a parameter with the highest GR relevance degree and a parameter with the lowest GR relevance degree, determine a standard well, and standardize a parameter with the highest GR relevance degree in the non-standard well and a parameter with the lowest GR relevance degree in the non-standard well by using the parameter with the highest GR relevance degree in the standard well and the parameter with the lowest GR relevance degree in the standard well;
a scatter diagram obtaining module 502, configured to obtain a corresponding scatter diagram according to a parameter with the highest GR correlation degree in the standard well, a parameter with the lowest GR correlation degree in the standard well, a parameter with the highest GR correlation degree in the normalized non-standard well, and a parameter with the lowest GR correlation degree in the normalized non-standard well;
and a limestone structure-cause type identification module 503, configured to identify a limestone structure-cause type according to the corresponding scatter diagram by combining the range threshold of the parameter with the highest GR correlation and the range threshold of the parameter with the lowest GR correlation.
According to the limestone structure-cause type identification device based on logging information, which is provided by the embodiment of the invention, the parameter standardization module, the scatter diagram acquisition module and the limestone structure-cause type identification module are adopted, the parameter with the highest correlation degree with GR in the standard well drilling and the parameter with the lowest correlation degree with GR in the non-standard well drilling are obtained, the parameter with the highest correlation degree with GR in the non-standard well drilling and the parameter with the lowest correlation degree are standardized on the basis, then the scatter diagram is constructed according to the standardized parameters, and the limestone structure-cause type can be effectively identified by means of the logging information in combination with the range threshold of the corresponding parameters.
The method of the embodiment of the invention is realized by depending on the electronic equipment, so that the related electronic equipment is necessarily introduced. To this end, an embodiment of the present invention provides an electronic apparatus, as shown in fig. 6, including: at least one processor (processor)601, a communication Interface (Communications Interface)604, at least one memory (memory)602, and a communication bus 603, wherein the at least one processor 601, the communication Interface 604, and the at least one memory 602 communicate with each other through the communication bus 603. The at least one processor 601 may invoke logic instructions in the at least one memory 602 to perform the following method: analyzing the natural gamma log GR to obtain a parameter with the highest GR relevance and a parameter with the lowest GR relevance, determining a standard well, and standardizing the parameter with the highest GR relevance in the non-standard well and the parameter with the lowest GR relevance in the non-standard well by adopting the parameter with the highest GR relevance in the standard well and the parameter with the lowest relevance in the standard well; acquiring a corresponding scatter diagram according to the parameter with the highest GR correlation degree in the standard well drilling, the parameter with the lowest GR correlation degree in the standard well drilling, the parameter with the highest GR correlation degree in the standardized non-standard well drilling and the parameter with the lowest GR correlation degree in the standardized non-standard well drilling; and identifying the limestone structure-cause type according to the corresponding scatter diagram by combining the range threshold of the parameter with the highest GR correlation degree and the range threshold of the parameter with the lowest GR correlation degree.
Furthermore, the logic instructions in the at least one memory 602 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. Examples include: analyzing the natural gamma log GR to obtain a parameter with the highest GR relevance and a parameter with the lowest GR relevance, determining a standard well, and standardizing the parameter with the highest GR relevance in the non-standard well and the parameter with the lowest GR relevance in the non-standard well by adopting the parameter with the highest GR relevance in the standard well and the parameter with the lowest relevance in the standard well; acquiring a corresponding scatter diagram according to the parameter with the highest GR correlation degree in the standard well drilling, the parameter with the lowest GR correlation degree in the standard well drilling, the parameter with the highest GR correlation degree in the standardized non-standard well drilling and the parameter with the lowest GR correlation degree in the standardized non-standard well drilling; and identifying the limestone structure-cause type according to the corresponding scatter diagram by combining the range threshold of the parameter with the highest GR correlation degree and the range threshold of the parameter with the lowest GR correlation degree. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A limestone structure-cause type identification method based on well logging information is characterized by comprising the following steps:
analyzing the natural gamma log GR to obtain a parameter with the highest GR relevance and a parameter with the lowest GR relevance, determining a standard well, and standardizing the parameter with the highest GR relevance in the non-standard well and the parameter with the lowest GR relevance in the non-standard well by adopting the parameter with the highest GR relevance in the standard well and the parameter with the lowest relevance in the standard well;
acquiring a corresponding scatter diagram according to the parameter with the highest GR correlation degree in the standard well drilling, the parameter with the lowest GR correlation degree in the standard well drilling, the parameter with the highest GR correlation degree in the standardized non-standard well drilling and the parameter with the lowest GR correlation degree in the standardized non-standard well drilling;
and identifying the limestone structure-cause type according to the corresponding scatter diagram by combining the range threshold of the parameter with the highest GR correlation degree and the range threshold of the parameter with the lowest GR correlation degree.
2. The method for identifying a limestone structure-cause type based on logging information as claimed in claim 1, wherein the parameter with the highest correlation with GR comprises:
uranium, potassium or thorium.
3. The method for identifying a limestone structure-cause type based on logging information as claimed in claim 1, wherein the parameters with the lowest correlation degree with GR comprise:
uranium, potassium or thorium.
4. The method for identifying a limestone structure-cause type based on logging information as claimed in claim 1, wherein the step of normalizing the parameter with the highest correlation with GR in the non-standard well and the parameter with the lowest correlation with GR in the non-standard well by using the parameter with the highest correlation with GR in the standard well and the parameter with the lowest correlation with GR in the standard well comprises the steps of:
obtaining the average value of the parameter with the highest GR relevance degree in the standard well drilling, obtaining the average value of the parameter with the highest standard well drilling and the average value of the parameter with the lowest GR relevance degree in the standard well drilling, obtaining the average value of the parameter with the lowest standard well drilling;
obtaining an average value of a parameter with the highest GR correlation degree in a non-standard well, obtaining an average value of the highest parameter of the non-standard well, and an average value of a parameter with the lowest GR correlation degree in the non-standard well, obtaining a minimum parameter average value of the non-standard well, obtaining a normalization coefficient of the parameter with the highest GR correlation degree in the non-standard well according to the average value of the highest parameter of the standard well and the average value of the highest parameter of the non-standard well, and obtaining a normalization coefficient of the parameter with the lowest GR correlation degree in the non-standard well according to the average value of the lowest parameter of the standard well and the average value of the lowest parameter of the non-standard;
normalizing the parameter with the highest GR correlation in the non-standard well according to the normalization coefficient of the parameter with the highest GR correlation in the non-standard well, and normalizing the parameter with the lowest GR correlation in the non-standard well according to the normalization coefficient of the parameter with the lowest GR correlation in the non-standard well.
5. The method for identifying a limestone structure-cause type based on logging information as claimed in claim 2, wherein the threshold range of the parameter with the highest GR correlation comprises:
less than 1.2 uranium, greater than 1.2 uranium and less than 2.5 uranium, or greater than 2.5 uranium.
6. The method for identifying a limestone structure-cause type based on logging information as claimed in claim 1, wherein the threshold range of the parameter with the lowest GR correlation comprises:
a uranium to thorium ratio of less than 1.5, a uranium to thorium ratio of greater than 1.5 to less than 4, or a uranium to thorium ratio of greater than 4.
7. A limestone structure-cause type identification device based on well logging data is characterized by comprising:
the parameter standardization module is used for analyzing the natural gamma logging GR, obtaining a parameter with the highest GR relevance and a parameter with the lowest GR relevance, determining a standard well, and standardizing the parameter with the highest GR relevance in the non-standard well and the parameter with the lowest GR relevance in the non-standard well by adopting the parameter with the highest GR relevance in the standard well and the parameter with the lowest GR relevance in the standard well;
the scatter diagram acquisition module is used for acquiring a corresponding scatter diagram according to the parameter with the highest GR correlation degree in the standard well drilling, the parameter with the lowest GR correlation degree in the standard well drilling, the parameter with the highest GR correlation degree in the standardized non-standard well drilling and the parameter with the lowest GR correlation degree in the standardized non-standard well drilling;
and the limestone structure-cause type identification module is used for identifying the limestone structure-cause type according to the corresponding scatter diagram by combining the range threshold of the parameter with the highest GR correlation and the range threshold of the parameter with the lowest GR correlation.
8. An electronic device, comprising:
at least one processor, at least one memory, a communication interface, and a bus; wherein,
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor, the processor calling the program instructions to perform the method of any of claims 1 to 6.
9. A non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the method of any one of claims 1-6.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4578579A (en) * | 1983-09-01 | 1986-03-25 | Mobil Oil Corporation | Method for depth referencing hydrocarbon gas shows on mud logs |
| CN103775057A (en) * | 2013-12-27 | 2014-05-07 | 中国石油天然气股份有限公司 | Method and device for identifying effective reservoir of tight oil and gas reservoir |
| CN105257284A (en) * | 2015-09-23 | 2016-01-20 | 中国石油天然气股份有限公司 | Method and device for determining content of coagulated ash by utilizing element capture energy spectrum logging |
| CN105275456A (en) * | 2014-06-23 | 2016-01-27 | 中国石油化工股份有限公司 | Method for identifying high-quality shale by using logging information |
| CN105822301A (en) * | 2016-03-23 | 2016-08-03 | 中国石油大学(北京) | Method for establishing logging prediction mathematic model about TOC (Total Organic Carbon) |
| WO2017058043A1 (en) * | 2015-09-28 | 2017-04-06 | Baker Hughes Incorporated | Correcting shale volume and measuring anisotropy in invaded zone |
| CN108072916A (en) * | 2016-11-16 | 2018-05-25 | 中国石油化工股份有限公司 | Lacustrine facies mud shale lithofacies Logging Identification Method based on genetic analysis |
-
2019
- 2019-01-24 CN CN201910067700.5A patent/CN109828311B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4578579A (en) * | 1983-09-01 | 1986-03-25 | Mobil Oil Corporation | Method for depth referencing hydrocarbon gas shows on mud logs |
| CN103775057A (en) * | 2013-12-27 | 2014-05-07 | 中国石油天然气股份有限公司 | Method and device for identifying effective reservoir of tight oil and gas reservoir |
| CN105275456A (en) * | 2014-06-23 | 2016-01-27 | 中国石油化工股份有限公司 | Method for identifying high-quality shale by using logging information |
| CN105257284A (en) * | 2015-09-23 | 2016-01-20 | 中国石油天然气股份有限公司 | Method and device for determining content of coagulated ash by utilizing element capture energy spectrum logging |
| WO2017058043A1 (en) * | 2015-09-28 | 2017-04-06 | Baker Hughes Incorporated | Correcting shale volume and measuring anisotropy in invaded zone |
| CN105822301A (en) * | 2016-03-23 | 2016-08-03 | 中国石油大学(北京) | Method for establishing logging prediction mathematic model about TOC (Total Organic Carbon) |
| CN108072916A (en) * | 2016-11-16 | 2018-05-25 | 中国石油化工股份有限公司 | Lacustrine facies mud shale lithofacies Logging Identification Method based on genetic analysis |
Non-Patent Citations (1)
| Title |
|---|
| 川东南地区龙马溪组底部海相页岩高GR峰沉积成因探讨;王冠平 等;《沉积学报》;20180630;第36卷(第6期);第1243-1354页 * |
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